《人工智能一种现代方法》第四版习题答案

admin管理员组

文章数量:1530844

2024年4月21日发(作者：)

Chapter 1

1.1 Define in

yo盯own

word: (a) intelligence

,

(b)

artificial intelligence

,

(c) agen

t.

•

lntelligence在fíit:

Dictionary defmitions of intelligence talk about "the capacity to acquire and apply

knowledge" or "the faculty

of thought and reason" or "the

ab诅tyto∞mprehend

and

profit仕om

experience." These are all reasonable answers

,

but

if

we want

some也ing

quantifiable we would use

something like

"the ability to apply knowledge in order to perform better in an environmen

t."

智能的字典定义有一种学习或应用知识的能力，一种思考和推理的本领，领会并且得益于经验的能

力，这些都是有道理的答案，但如果我们想量化一些东西，我们将用到一些东西像为了在环境中更

好的完成任务使能力适应知识

•

Artific切1intelligenceλ工在f

íili:

We define

art出cial

intelligence as the study and construction of agent

programs阳t

perform well in a given environment

,

for a given agent

archit民ture.

作为一学习和构造智能体程序，为了一个智能体结构，在被给的环境中可以很好的完成任务。

• Agen

1Jf

íiIi体t:

We define an agent as an

entity实体that

takes action in response to percepts from an

environment.在一个环境中对一个对象做出反应的实体

1.4

Th

ere are well-known classes of problem that are intractably difficnlt for computers

,

and other

classes

that are provably undecidable. Does this mean that AI is impossible?

No. It

means也at

AI systems should

avoid位ying

to solve intractable problems.

Usually，由ism巳ans

they can

only approximate optimal behavi

or. Notice that humans don't solve NP complete problems either. Sometimes

由ey

are good at solving

spec诅c

instances

wi也a

lot of structure

,

perhaps with the aid of background

knowledge. AI systems should attempt to do

the same.

1.11

"surely computers cannot be intelligent-they can do only what their programmers tell them." Is

the latter statement true

,

and does it imply the former?

This depends

on your definition of "intelligen

t"

and "tell."

In

one sense computers

0世y

do

what自己

programmers command them to do

,

but in another sense

what也e

programmers consciously tells the

computer to

do often has very little to do with what the computer actually

do巳s.

An

yone who has written a

program wi

th an

orneηbug

knows this

,

as does anyone who has written a successful machine learning

program.

So in one sense Samuel

"told"也e

computer "learn to play checkers better than 1 do

,

and then play

that way

,"

but in another sense he told the computer "follow this learning algorithm" and it learned to play.

So

we're left in the situation where you may or may not consider learning to play checkers to be s sign of

intelligence (or you

may由ink白at

learning to play

in也e

right way

requ让es

intelligence

,

but not

in也is

way)

,

andyou

may吐血汰出e

intelligence resides in the programmer or in the computer

Chapter 2

2.1 Defme in

yo町own

words the following terms: agent

,

agent function

,

agent program

,

rationality

,

reflex agent

,

model-based agent

,

goal-based agent

,

utility-based agent

,

learning agent.

Th

e following are just some of the many possible defmitions that can be written:

•

Agentli'舷佯:

an entity

(实体)

tbat perceives

(感知)

and

acω行为;

or

,

one that can be viewed

as

perceiving and acting.

Essentially本质上any

object

qualifies限定;

the key point is the way the object

implements an agent function.

(N

ote: some autbors restrict the term to programs that operate on behaif of a

buman

,

or to programs that can cause some or

all

of their code to run on other machines on a networ

k,

as in

mobile agents. MOBILE AGENT)

一个具有感知和行文的实体，或者是一个可以观察到感觉的实体，本质上，任何限定对象，只要的观

点是一种对象执行智能体函数的方法。(注意，一些作者〉

可以感知环境，并在环境中行动的某种东西。

• Agent

function暂黯体函数:

a

function也at

specifies the agent' s action in

response归巳very

possible

p町ceptsequence智能体相应任何感知序列所采取的行动

• Agent program

tf:

f

fif体程序:

that program whicb

,

combined with a machine architecture

,

implements an

agent function.

In

0世simple

designs

,

tbe program takes a new percept on eacb invocation and returns an

ac挝on.实现了智能函数。有各种基本的智能体程序设计，反应出现实表现的一级用于决策过程的信息

种类。设计可能在效率、压缩性和灵活性方面有变化。适当的智能体程序设计取决于环境的本性

•

Rationali句;王军放:

a property of agents that choose actions that maximize tbeir expected

u创坷，

given the

percepts to date.

•

Autonomy

fJ主:

a property of

agenωwhose

bebavior is determined by tbeir own experience rather than

solely by their initial programming.

.R伪'xagent反射却在FSE体:

an agent whose action depends only on the current percept.

一个智能体的行为仅仅依赖于当前的知觉。

• Model-based

agent基于茹苦型的主FifS体:

an agent wbose actioD is derived directly from an internal model

ofthe

c田rent

world state that is updated over time.

一个智能体的行为直接得自于内在模型的状态，这个状态是当前世界通用的不断更新。

• Goal-based

agen基'FfU萃的苟能俐:

an agent that selects actions that it believes will acbieve explicitly

represented

goals.智能体选择它相信能明确达到目标的行动。

•

Utility-bωed agen基于效用的主F磁仰:

an agent that

sel饵ts

actions that it

bel即es

will

maximize也e

expected

util即of

the outcome

state.试图最大化他们自己期望的快乐

• Learning

agent学习智能做:

an agent whose behavior improves over time based on its experience.

2.2 Both the performance

m阅sure

and the utility function

me描盯e

how well an agent

is

doing.

Explain the difference between the two.

A performance

measure

(性能度量)

is used by an outside observer to evaluate

(评估)

bow successful an

agent is. It is a function from bistories to a real numbe

r.

A

ut诅ity

function

(效用函数)

is used by an agent

its

elf

ωevaluate

how desirable

(令人想要)

states or bistories are.

In

our framewor

k,

the

ut让ity

function

m

ay not be the same as the performance measure;

fl町由ermore，

an agent may have no

expliαt u创ity

function at all

,

whereas there is always a

perfo口nance

measure.

following agents

,

develop a PEAS description of the task environment:

2.5

For each of

a. Robot soccer player;

b.

In

ternet book-shopping agent;

c. Autonomous Mars rover;

d.

Mathema创cian's

theorem-proving assistant.

Some representative

,

but not

e对laustive，

answers are given in Figure S2.

1.

本文标签： 智能环境方法状态实体